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QSP Modeling for Biologics Discovery

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Better early-stage decisions start with mechanistic insight

Early-stage biologics discovery is filled with big decisions with substantial uncertainty.

Despite limited data, Teams must choose:

Which targets to pursue
Which candidates to prioritize
What properties to optimize
Which programs deserve continued investment

Certara’s discovery-stage mechanistic modeling supports target identification and validation in drug discovery, alongside molecular design requirements based on the pharmacological concept with little to no data required.

These early evaluations of biotherapeutic design enable more confident go/no-go decisions by defining optimal drug properties for the intended mechanism of action and identifying developability risks. The quantitative framework supplements intuition and limited experimental data to enrich a pipeline with viable assets through better decision-making.

What is Early Feasibility Assessment?

Early Feasibility Assessment (EFA) is a workflow designed for the earliest stages of biologics discovery, to answer a fundamental pharmacology question: Can a drug work as intended in vivo?

Using inputs derived from literature or early in vitro data, EFA uses mechanistic pharmacology models to predict what design properties a biologic must have to achieve efficacious binding at a relevant site of action in the human body. Such simulations can be used to triage targets, support lead optimization in drug discovery, and prioritize early portfolios, even before a single molecule is synthesized.

The impact: more efficient resource allocation, accelerated development timelines, and reduced late-stage failure, ultimately delivering novel treatments to patients faster.

Diagram showing two modeling approaches: the first combines known drug properties and target properties to predict efficacious binding levels, yielding validated predictions; the second rearranges the same models to determine required drug properties from known target properties and efficacious binding levels.

Mechanistic models for the full range of biologic formats

Pre-built and validated model packs are available for a wide range of drug modalities, enabling accelerated model-informed decision-making at the pace discovery demands.

单克隆抗体

Bispecific antibodies

Antibody-drug conjugates (ADC)

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T-cell engagers

Masked / conditionally active biologics

Targeted protein degraders (MGDs/PROTACs)

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Fusion proteins

Soluble target modulators

Cell surface receptor agonists / antagonists

Five ways EFA transforms early biologics discovery

From individual molecule assessment to portfolio-wide triaging, early mechanistic insight reduces risk and improves efficiency across discovery and early development.

1. Drug design optimization

Identify the specific drug properties (e.g. affinity, half-life, valency) that enable a biologic to achieve its pharmacological goal. Rather than using trial-and-error in the lab, protein engineers receive quantitative design targets derived from simulation, with a clear understanding of how each parameter affects target interaction in vivo, accelerating lead optimization in drug discovery before a single molecule is made.

Model-informed Target Product Profile (TPP) definition
Affinity and half-life design space mapping
Valency and format trade-off analysis (e.g., 1×1 vs. 2×2 bispecifics)
Dosing route/interval feasibility (e.g. SC vs. IV, Q4W vs. Q8W)

Heatmap of molecular design and dosing requirements for a bispecific antibody (bsAb)

Antibody affinity heatmaps comparing unfavorable, risky, and favorable target binding profiles

2. Lead candidate selection

When multiple drug concepts or molecules are under evaluation, EFA provides an objective, pharmacology-based ranking. Candidates are assessed against developability criteria, distinguishing the most favorable from the infeasible, and highlighting which molecules carry the greatest risk before committing resources to their production.

Candidate triage based on predicted in vivo target engagement
Developability ranking (most favorable → infeasible)
Competitor benchmarking and differentiation analysis
Context-dependent pharmacology assessment (e.g., TMDD, masking)

3. Portfolio prioritization

EFA scales to portfolio-level decision-making, making portfolio prioritization achievable for pharma sponsors of any size, triaging dozens of drug concepts simultaneously. By simulating millions of parameter combinations, affinities, half-lives, valencies, dosing regimens, across entire portfolios, EFA provides a rational basis for resource allocation before any molecules are produced.

Industrial-scale biosimulation across large candidate sets
Go/no-go recommendations based on pharmacological and developability evidence
Integrated clinical evidence assessment per concept
Pivot planning when lead concepts underperform

Landscape scatter chart comparing drug modality developability and therapeutic index

4. Modality selection

Choosing the right drug modality – monoclonal antibody, bispecific, ADC, T-cell engager, or degrader – is a critical early decision that profoundly shapes PK, safety, and manufacturing complexity. EFA uses mechanistic models to compare modalities head-to-head on their predicted in vivo pharmacology, enabling evidence-based format selection before platform investment.

Comparative in vivo pharmacology across modality formats
Assessment of complex mechanisms: masking, conditional activation, avidity
Bispecific, multispecific, and novel modality feasibility
Therapeutic index evaluation by format

5. Animal study design optimization

EFA directly supports the 3Rs (Replace, Reduce, Refine) in nonhuman primate (NHP) and other animal studies by using simulation to inform dose selection, interval, and sample collection timing, before any animals are used. Mathematical modeling can predict which dose levels and time points will yield the most valuable pharmacokinetic and pharmacodynamic information, enabling more efficient study designs that use fewer animals without sacrificing scientific value.

By simulating PK and target engagement profiles across candidate dose levels, teams can plan studies that distinguish TMDD from linear PK, optimize sampling schedules to capture key transitions, and avoid redundant dose groups that provide limited additional information.

Dose selection for differentiated PK characterization (linear vs. TMDD)
NHP study design with reduced animal use (3Rs compliance)
Sampling time point optimization to maximize data value
Prediction of target engagement at each dose level prior to study initiation

Cynomolgus monkey pharmacokinetics and target engagement charts across antibody dose levels

Committed to the 3Rs? Go further with Non-Animal Navigator™

EFA’s simulation-driven approach to animal study design pairs naturally with Certara’s Non-Animal Navigator™, a solution purpose-built to help sponsors reduce reliance on animal testing across development. Explore how it complements your EFA workflow.

Related offerings

选择 Certara 定量系统药理学 (QSP) 咨询服务，利用计算建模和实验数据加强药物开发。

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Certara IQ 是一款基于人工智能的 QSP 建模软件，能够扩展定量系统药理学应用，提供经过验证的模型、工作流程及符合监管要求的洞察。

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指南

From concept to confident decision

Leading pharma sponsors have used Certara’s EFA framework for portfolio prioritization, design optimization, and redirected resources before a single molecule was built.

See how it works in practice. Our EFA guide walks through real case studies, from bispecific antibody design to portfolio prioritization and novel ADC development, showing exactly how sponsors turn early feasibility questions into confident go/no-go decisions.

Download the EFA Guide

Certara IQ™ Explore — purpose-built for discovery-stage QSP

EFA is delivered using Certara IQ™ Explore, a QSP software platform with pre-built and validated mechanistic model packs covering a broad range of biologic modalities. The platform enables rapid simulation deployment—critical when time to actionable results is measured in weeks, not months.

Pre-built, validated model packs for diverse biologic modalities
Industrial-scale simulation capacity
Instant visualization of PK, target engagement, and feasibility outputs
Models are reusable as new questions arise throughout discovery
Available as licensed software or as a Certara-led consultancy engagement
Option for custom model delivery within Explore

认识我们的专家团队

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专家

Marc Presler, PhD

Director, QSP

Meet Marc Presler, a member of Certara QSP’s Team! Marc has leveraged deep expertise in fundamental biology and quantitation to support over 70 drug programs. He sees models as powerful tools that help integrate evidence to improve decision-making in drug development. His contributions have ranged from clinical trial design, first-in-human...

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专家

Sarah DiBartolo

Associate Principal Scientist, QSP

With 10+ years experience, Sarah’s expertise in QSP modeling spans the entire drug development pipeline, from early feasibility assessment to clinical and post-market analyses, with a focus on Alzheimer’s Disease.

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QSP 卓越中心

依托 Certara QSP 团队解答关于靶点优先排序、剂量确定、疗效或毒性评估、市场竞争地位分析等关键问题。

与 QSP 专家合作

350

年累计经验

275

家客户

700

个 QSP 项目

70

位 QSP 科学家

Ready to evaluate your biologic’s pharmacological feasibility?

Talk to a Certara QSP expert to discuss how Early Feasibility Assessment can be applied to your discovery program, whether you are conducting target identification and validation, pursuing lead optimization in drug design, or handling portfolio prioritization for your pharma organization.